Re: New Defects reported by Coverity Scan for XenProject

[Alejandro Vallejo <alejandro.garciavallejo@amd.com>]

On Mon Sep 8, 2025 at 1:25 PM CEST, Jan Beulich wrote:
> On 08.09.2025 13:04, Alejandro Vallejo wrote:
>> On Mon Sep 8, 2025 at 12:19 PM CEST, Jan Beulich wrote:
>>> On 07.09.2025 16:37, scan-admin@coverity.com wrote:
>>>> ** CID 1665362:       Integer handling issues  (INTEGER_OVERFLOW)
>>>> /xen/lib/find-next-bit.c: 104           in find_next_zero_bit()
>>>>
>>>>
>>>> _____________________________________________________________________________________________
>>>> *** CID 1665362:         Integer handling issues  (INTEGER_OVERFLOW)
>>>> /xen/lib/find-next-bit.c: 104             in find_next_zero_bit()
>>>> 98     	}
>>>> 99     	if (!size)
>>>> 100     		return result;
>>>> 101     	tmp = *p;
>>>> 102     
>>>> 103     found_first:
>>>>>>>     CID 1665362:         Integer handling issues  (INTEGER_OVERFLOW)
>>>>>>>     Expression "0xffffffffffffffffUL << size", where "size" is known to be equal to 63, overflows the type of "0xffffffffffffffffUL << size", which is type "unsigned long".
>>>> 104     	tmp |= ~0UL << size;
>>>> 105     	if (tmp == ~0UL)	/* Are any bits zero? */
>>>> 106     		return result + size;	/* Nope. */
>>>> 107     found_middle:
>>>> 108     	return result + ffz(tmp);
>>>> 109     }
>>>
>>> I cannot make sense of their claim. 0xffffffffffffffffUL << 63 is simply
>>> 0x8000000000000000UL, isn't it? Where's the overflow there? There also
>>> cannot be talk of a 32-bit build, or else ~0UL would have been transformed
>>> to 0xffffffffUL.
>> 
>> The offending line LGTM too.
>> 
>> The only credible explanation I can think of is Coverity flagging discarded 1s
>> on left shifts as loss of precision.
>> 
>> If so, "~((1 << size) - 1)", or "(~0UL >> size) << size" should make it happy,
>> but surely that error would flare up with all uses of GENMASK() too?
>
> And with any other non-zero values of "size" here.
>
> Jan

Is this the only issue flagged? Or are there any others of the same kind? It
might me easier to spot a pattern with a larger dataset.

Cheers,
Alejandro